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Overview

Dataset statistics

Number of variables39
Number of observations33917
Missing cells707850
Missing cells (%)53.5%
Duplicate rows69
Duplicate rows (%)0.2%
Total size in memory10.1 MiB
Average record size in memory312.0 B

Variable types

Categorical26
Boolean7
Numeric6

Warnings

Dataset has 69 (0.2%) duplicate rows Duplicates
AdditionalInformation has a high cardinality: 2036 distinct values High cardinality
Address has a high cardinality: 4178 distinct values High cardinality
ClearanceSpace has a high cardinality: 112 distinct values High cardinality
CouncilAdditionalInformation has a high cardinality: 1471 distinct values High cardinality
CouncilClearanceSpace has a high cardinality: 113 distinct values High cardinality
CouncilGenus has a high cardinality: 226 distinct values High cardinality
ElectricalAssets has a high cardinality: 83 distinct values High cardinality
Genus has a high cardinality: 294 distinct values High cardinality
InspectionDatetime has a high cardinality: 60 distinct values High cardinality
Locality has a high cardinality: 1388 distinct values High cardinality
LocationFeeder has a high cardinality: 660 distinct values High cardinality
CouncilClearanceRange is highly correlated with HazardAssessment and 1 other fieldsHigh correlation
ElectricLineContact is highly correlated with CouncilOtherInfrastructurePresent and 3 other fieldsHigh correlation
ElectricalAssets is highly correlated with HazardAssessmentHigh correlation
NetworkType is highly correlated with ResponsibleCouncilHigh correlation
CouncilOtherInfrastructurePresent is highly correlated with ElectricLineContact and 7 other fieldsHigh correlation
NonComplianceCode is highly correlated with CouncilOtherInfrastructurePresent and 2 other fieldsHigh correlation
CouncilNonComplianceCode is highly correlated with HazardAssessment and 1 other fieldsHigh correlation
TemperatureRange is highly correlated with NonCompliantHigh correlation
OtherInfrastructurePresent is highly correlated with CouncilOtherInfrastructurePresent and 3 other fieldsHigh correlation
ClearanceRange is highly correlated with CouncilOtherInfrastructurePresent and 3 other fieldsHigh correlation
CouncilSingleOrMultipleTrees is highly correlated with HazardAssessment and 1 other fieldsHigh correlation
FinancialYear is highly correlated with ResponsibleCouncilHigh correlation
CouncilElectricLineContact is highly correlated with HazardAssessment and 3 other fieldsHigh correlation
ResponsibleCouncil is highly correlated with NetworkType and 3 other fieldsHigh correlation
NonCompliant is highly correlated with ElectricLineContact and 6 other fieldsHigh correlation
HazardAssessment is highly correlated with CouncilClearanceRange and 11 other fieldsHigh correlation
FireHazardDeclaredStatus is highly correlated with CouncilElectricLineContact and 1 other fieldsHigh correlation
SingleOrMultipleTrees is highly correlated with CouncilOtherInfrastructurePresent and 3 other fieldsHigh correlation
ProgramType is highly correlated with CouncilElectricLineContact and 1 other fieldsHigh correlation
SpanVoltages is highly correlated with CouncilOtherInfrastructurePresent and 2 other fieldsHigh correlation
CouncilSpanVoltages is highly correlated with HazardAssessment and 1 other fieldsHigh correlation
VegetationSpan is highly correlated with CouncilClearanceRange and 12 other fieldsHigh correlation
AdditionalInformation has 31464 (92.8%) missing values Missing
Address has 29590 (87.2%) missing values Missing
ClearanceRange has 31768 (93.7%) missing values Missing
ClearanceSpace has 32428 (95.6%) missing values Missing
CouncilAdditionalInformation has 31950 (94.2%) missing values Missing
CouncilClearanceRange has 32085 (94.6%) missing values Missing
CouncilClearanceSpace has 32057 (94.5%) missing values Missing
CouncilElectricLineContact has 32047 (94.5%) missing values Missing
CouncilGenus has 31950 (94.2%) missing values Missing
CouncilNonComplianceCode has 31950 (94.2%) missing values Missing
CouncilOtherInfrastructurePresent has 31950 (94.2%) missing values Missing
CouncilSingleOrMultipleTrees has 31950 (94.2%) missing values Missing
CouncilSpanVoltages has 31950 (94.2%) missing values Missing
ElectricLineContact has 32166 (94.8%) missing values Missing
ElectricalAssets has 3868 (11.4%) missing values Missing
Genus has 31728 (93.5%) missing values Missing
LocationFeeder has 3913 (11.5%) missing values Missing
NonComplianceCode has 31461 (92.8%) missing values Missing
NonCompliant has 3591 (10.6%) missing values Missing
OtherInfrastructurePresent has 31722 (93.5%) missing values Missing
ResponsibleCouncil has 31949 (94.2%) missing values Missing
SingleOrMultipleTrees has 31728 (93.5%) missing values Missing
SpanID1 has 2631 (7.8%) missing values Missing
SpanID2 has 2647 (7.8%) missing values Missing
SpanLength has 29857 (88.0%) missing values Missing
SpanVoltages has 31461 (92.8%) missing values Missing
TemperatureRange has 25989 (76.6%) missing values Missing
SpanID1 is highly skewed (γ1 = 31.73289658) Skewed
SpanID2 is highly skewed (γ1 = 32.62134889) Skewed
SpanLength is highly skewed (γ1 = 23.88842538) Skewed
Address is uniformly distributed Uniform

Reproduction

Analysis started2021-03-23 03:55:03.770044
Analysis finished2021-03-23 03:55:28.338189
Duration24.57 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

AdditionalInformation
Categorical

HIGH CARDINALITY
MISSING

Distinct2036
Distinct (%)83.0%
Missing31464
Missing (%)92.8%
Memory size265.1 KiB
Tree growing within clearance space below LV
 
21
Multiple trees growing within clearance space below LV
 
21
Shire tree in contact with LV
 
16
Shire tree growing under LV
 
15
Private tree growing under LV
 
15
Other values (2031)
2365 

Length

Max length189
Median length52
Mean length56.10313901
Min length4

Characters and Unicode

Total characters137621
Distinct characters78
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1895 ?
Unique (%)77.3%

Sample

1st rowLIS 408654 - 408659. Eucalyptus less than 1.5m from LV, blowing closer in wind
2nd rowPrivate Jacaranda at #12 less than 0.5 meters beside LV
3rd rowPrivate eucalyptus at #6 strucktural limb less than 1 meter beside lv
4th rowPrivate tree at 8 Ashford st signs of contact with HV
5th rowPrivate Cypress less than 1.0m beside LV
ValueCountFrequency (%)
Tree growing within clearance space below LV21
 
0.1%
Multiple trees growing within clearance space below LV21
 
0.1%
Shire tree in contact with LV16
 
< 0.1%
Shire tree growing under LV15
 
< 0.1%
Private tree growing under LV15
 
< 0.1%
Vegetation in clearance space to side of HV14
 
< 0.1%
Private tree growing to side of LV14
 
< 0.1%
Vegetation contacting LV13
 
< 0.1%
Private tree in contact with LV13
 
< 0.1%
Multiple trees growing within clearance space of LV12
 
< 0.1%
Other values (2026)2299
 
6.8%
(Missing)31464
92.8%
2021-03-23T14:55:28.674089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lv1498
 
6.3%
below1044
 
4.4%
hv913
 
3.8%
clearance908
 
3.8%
to778
 
3.3%
space755
 
3.2%
in754
 
3.2%
of752
 
3.1%
and693
 
2.9%
growing671
 
2.8%
Other values (1704)15119
63.3%

Most occurring characters

ValueCountFrequency (%)
21715
15.8%
e11693
 
8.5%
a8707
 
6.3%
n7904
 
5.7%
i7448
 
5.4%
t6866
 
5.0%
o6628
 
4.8%
s6175
 
4.5%
r6134
 
4.5%
c5510
 
4.0%
Other values (68)48841
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter94975
69.0%
Space Separator21715
 
15.8%
Uppercase Letter10732
 
7.8%
Decimal Number7455
 
5.4%
Other Punctuation2162
 
1.6%
Dash Punctuation356
 
0.3%
Math Symbol170
 
0.1%
Connector Punctuation30
 
< 0.1%
Open Punctuation13
 
< 0.1%
Close Punctuation13
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
V2640
24.6%
L1936
18.0%
H976
 
9.1%
P865
 
8.1%
S688
 
6.4%
E638
 
5.9%
I512
 
4.8%
C413
 
3.8%
M411
 
3.8%
A358
 
3.3%
Other values (16)1295
12.1%
ValueCountFrequency (%)
e11693
12.3%
a8707
 
9.2%
n7904
 
8.3%
i7448
 
7.8%
t6866
 
7.2%
o6628
 
7.0%
s6175
 
6.5%
r6134
 
6.5%
c5510
 
5.8%
l5029
 
5.3%
Other values (16)22881
24.1%
ValueCountFrequency (%)
11632
21.9%
01156
15.5%
3805
10.8%
5788
10.6%
2714
9.6%
9501
 
6.7%
8482
 
6.5%
6473
 
6.3%
4470
 
6.3%
7434
 
5.8%
ValueCountFrequency (%)
.1575
72.8%
&345
 
16.0%
,105
 
4.9%
#94
 
4.3%
"18
 
0.8%
/15
 
0.7%
:5
 
0.2%
'2
 
0.1%
*2
 
0.1%
@1
 
< 0.1%
ValueCountFrequency (%)
21715
100.0%
ValueCountFrequency (%)
-356
100.0%
ValueCountFrequency (%)
_30
100.0%
ValueCountFrequency (%)
(13
100.0%
ValueCountFrequency (%)
)13
100.0%
ValueCountFrequency (%)
<170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin105707
76.8%
Common31914
 
23.2%

Most frequent character per script

ValueCountFrequency (%)
e11693
 
11.1%
a8707
 
8.2%
n7904
 
7.5%
i7448
 
7.0%
t6866
 
6.5%
o6628
 
6.3%
s6175
 
5.8%
r6134
 
5.8%
c5510
 
5.2%
l5029
 
4.8%
Other values (42)33613
31.8%
ValueCountFrequency (%)
21715
68.0%
11632
 
5.1%
.1575
 
4.9%
01156
 
3.6%
3805
 
2.5%
5788
 
2.5%
2714
 
2.2%
9501
 
1.6%
8482
 
1.5%
6473
 
1.5%
Other values (16)2073
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII137621
100.0%

Most frequent character per block

ValueCountFrequency (%)
21715
15.8%
e11693
 
8.5%
a8707
 
6.3%
n7904
 
5.7%
i7448
 
5.4%
t6866
 
5.0%
o6628
 
4.8%
s6175
 
4.5%
r6134
 
4.5%
c5510
 
4.0%
Other values (68)48841
35.5%

Address
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct4178
Distinct (%)96.6%
Missing29590
Missing (%)87.2%
Memory size265.1 KiB
CRN Cooke St & Hamilton St, Bittern
 
9
1 Cook Rd, HMAS Cerberus
 
9
2 Pettit St, CRIB POINT
 
7
Fire Track beside #1 Library Rd, Balnarring Beach
 
7
62 Stuart Rd, Tyabb
 
6
Other values (4173)
4289 

Length

Max length194
Median length31
Mean length38.98405362
Min length2

Characters and Unicode

Total characters168684
Distinct characters76
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4077 ?
Unique (%)94.2%

Sample

1st rowOpposite 134 Main St, Sebastian
2nd row38, 40 & 42 Manningtree Rd, Hawthorn
3rd row14 & 12 Hansen st Kew
4th row12 & 10 & 8 Hensen st Kew
5th row20 College parade Kew
ValueCountFrequency (%)
CRN Cooke St & Hamilton St, Bittern9
 
< 0.1%
1 Cook Rd, HMAS Cerberus9
 
< 0.1%
2 Pettit St, CRIB POINT7
 
< 0.1%
Fire Track beside #1 Library Rd, Balnarring Beach7
 
< 0.1%
62 Stuart Rd, Tyabb6
 
< 0.1%
13&15 Russell Street, Newtown6
 
< 0.1%
Cook Rd, HMAS Cerberus6
 
< 0.1%
51 Flinders St, Bittern5
 
< 0.1%
Blue Johanna Rd, Johanna3
 
< 0.1%
Opp 36 Sth Gippsland Hwy, Tooradin. In roadside reserve between service road and highway.3
 
< 0.1%
Other values (4168)4266
 
12.6%
(Missing)29590
87.2%
2021-03-23T14:55:29.076641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rd1863
 
6.3%
st1811
 
6.1%
of982
 
3.3%
in542
 
1.8%
property532
 
1.8%
443
 
1.5%
and438
 
1.5%
side393
 
1.3%
opposite287
 
1.0%
dr271
 
0.9%
Other values (4100)22007
74.4%

Most occurring characters

ValueCountFrequency (%)
25540
 
15.1%
e11318
 
6.7%
r9667
 
5.7%
o9420
 
5.6%
a8722
 
5.2%
t8302
 
4.9%
n8284
 
4.9%
d5508
 
3.3%
i5435
 
3.2%
l5138
 
3.0%
Other values (66)71350
42.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter98927
58.6%
Uppercase Letter25860
 
15.3%
Space Separator25540
 
15.1%
Decimal Number10618
 
6.3%
Other Punctuation7158
 
4.2%
Dash Punctuation267
 
0.2%
Open Punctuation157
 
0.1%
Close Punctuation156
 
0.1%
Connector Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
R3383
13.1%
S3346
12.9%
C1839
 
7.1%
A1579
 
6.1%
M1439
 
5.6%
B1400
 
5.4%
E1287
 
5.0%
O1165
 
4.5%
D1131
 
4.4%
N1087
 
4.2%
Other values (16)8204
31.7%
ValueCountFrequency (%)
e11318
11.4%
r9667
9.8%
o9420
9.5%
a8722
 
8.8%
t8302
 
8.4%
n8284
 
8.4%
d5508
 
5.6%
i5435
 
5.5%
l5138
 
5.2%
s5048
 
5.1%
Other values (16)22085
22.3%
ValueCountFrequency (%)
12074
19.5%
21554
14.6%
31222
11.5%
5988
9.3%
4969
9.1%
0865
8.1%
6834
7.9%
7764
 
7.2%
8710
 
6.7%
9638
 
6.0%
ValueCountFrequency (%)
,4705
65.7%
.1634
 
22.8%
&384
 
5.4%
"240
 
3.4%
#93
 
1.3%
?51
 
0.7%
/34
 
0.5%
'11
 
0.2%
*6
 
0.1%
ValueCountFrequency (%)
25540
100.0%
ValueCountFrequency (%)
-267
100.0%
ValueCountFrequency (%)
(157
100.0%
ValueCountFrequency (%)
)156
100.0%
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin124787
74.0%
Common43897
 
26.0%

Most frequent character per script

ValueCountFrequency (%)
e11318
 
9.1%
r9667
 
7.7%
o9420
 
7.5%
a8722
 
7.0%
t8302
 
6.7%
n8284
 
6.6%
d5508
 
4.4%
i5435
 
4.4%
l5138
 
4.1%
s5048
 
4.0%
Other values (42)47945
38.4%
ValueCountFrequency (%)
25540
58.2%
,4705
 
10.7%
12074
 
4.7%
.1634
 
3.7%
21554
 
3.5%
31222
 
2.8%
5988
 
2.3%
4969
 
2.2%
0865
 
2.0%
6834
 
1.9%
Other values (14)3512
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII168684
100.0%

Most frequent character per block

ValueCountFrequency (%)
25540
 
15.1%
e11318
 
6.7%
r9667
 
5.7%
o9420
 
5.6%
a8722
 
5.2%
t8302
 
4.9%
n8284
 
4.9%
d5508
 
3.3%
i5435
 
3.2%
l5138
 
3.0%
Other values (66)71350
42.3%

ClearanceRange
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31768
Missing (%)93.7%
Memory size66.4 KiB
True
 
1452
False
 
697
(Missing)
31768 
ValueCountFrequency (%)
True1452
 
4.3%
False697
 
2.1%
(Missing)31768
93.7%
2021-03-23T14:55:29.198025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

ClearanceSpace
Categorical

HIGH CARDINALITY
MISSING

Distinct112
Distinct (%)7.5%
Missing32428
Missing (%)95.6%
Memory size265.1 KiB
0
470 
200
 
45
600
 
44
700
 
44
400
 
36
Other values (107)
850 

Length

Max length15
Median length3
Mean length2.36937542
Min length1

Characters and Unicode

Total characters3528
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)1.1%

Sample

1st row250
2nd row850
3rd row0
4th row650
5th row850
ValueCountFrequency (%)
0470
 
1.4%
20045
 
0.1%
60044
 
0.1%
70044
 
0.1%
40036
 
0.1%
50035
 
0.1%
30031
 
0.1%
80029
 
0.1%
10027
 
0.1%
75025
 
0.1%
Other values (102)703
 
2.1%
(Missing)32428
95.6%
2021-03-23T14:55:29.453212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0470
31.5%
20045
 
3.0%
60044
 
2.9%
70044
 
2.9%
40036
 
2.4%
50035
 
2.3%
30031
 
2.1%
80029
 
1.9%
10027
 
1.8%
25025
 
1.7%
Other values (106)707
47.4%

Most occurring characters

ValueCountFrequency (%)
01798
51.0%
5315
 
8.9%
7207
 
5.9%
8204
 
5.8%
6186
 
5.3%
2183
 
5.2%
3177
 
5.0%
4176
 
5.0%
1145
 
4.1%
9108
 
3.1%
Other values (16)29
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3499
99.2%
Lowercase Letter22
 
0.6%
Space Separator4
 
0.1%
Uppercase Letter3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
m4
18.2%
t4
18.2%
s2
9.1%
h2
9.1%
a2
9.1%
n2
9.1%
l1
 
4.5%
e1
 
4.5%
o1
 
4.5%
c1
 
4.5%
Other values (2)2
9.1%
ValueCountFrequency (%)
01798
51.4%
5315
 
9.0%
7207
 
5.9%
8204
 
5.8%
6186
 
5.3%
2183
 
5.2%
3177
 
5.1%
4176
 
5.0%
1145
 
4.1%
9108
 
3.1%
ValueCountFrequency (%)
C1
33.3%
L1
33.3%
V1
33.3%
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3503
99.3%
Latin25
 
0.7%

Most frequent character per script

ValueCountFrequency (%)
m4
16.0%
t4
16.0%
s2
 
8.0%
h2
 
8.0%
a2
 
8.0%
n2
 
8.0%
l1
 
4.0%
e1
 
4.0%
C1
 
4.0%
o1
 
4.0%
Other values (5)5
20.0%
ValueCountFrequency (%)
01798
51.3%
5315
 
9.0%
7207
 
5.9%
8204
 
5.8%
6186
 
5.3%
2183
 
5.2%
3177
 
5.1%
4176
 
5.0%
1145
 
4.1%
9108
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3528
100.0%

Most frequent character per block

ValueCountFrequency (%)
01798
51.0%
5315
 
8.9%
7207
 
5.9%
8204
 
5.8%
6186
 
5.3%
2183
 
5.2%
3177
 
5.0%
4176
 
5.0%
1145
 
4.1%
9108
 
3.1%
Other values (16)29
 
0.8%

CouncilAdditionalInformation
Categorical

HIGH CARDINALITY
MISSING

Distinct1471
Distinct (%)74.8%
Missing31950
Missing (%)94.2%
Memory size265.1 KiB
Council tree less than 1.0m below LV
 
45
Council tree less than 0.5m below LV
 
31
Council tree growing below LV
 
28
Council Melaleuca in clearance space below LV
 
23
Council trees less than 0.5m below LV
 
17
Other values (1466)
1823 

Length

Max length219
Median length49
Mean length57.88866294
Min length21

Characters and Unicode

Total characters113867
Distinct characters76
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1329 ?
Unique (%)67.6%

Sample

1st rowMultiple trees in contact with LV
2nd rowCouncil Lophostemon less than 1 meter below LV
3rd rowcouncil trees less than 1 meter below and beside LV
4th rowCouncil tree kess than 0.5 meters above LV
5th rowcouncil Melaleuca at #8 less than 0.5 metrers from lv including stuctual limbs
ValueCountFrequency (%)
Council tree less than 1.0m below LV45
 
0.1%
Council tree less than 0.5m below LV31
 
0.1%
Council tree growing below LV28
 
0.1%
Council Melaleuca in clearance space below LV23
 
0.1%
Council trees less than 0.5m below LV17
 
0.1%
Council melaleuca growing under LV14
 
< 0.1%
Council tree contacting LV13
 
< 0.1%
Council melaleuca growing below LV13
 
< 0.1%
Council Melaleuca less than 1.0m below LV13
 
< 0.1%
Council tree below LV11
 
< 0.1%
Other values (1461)1759
 
5.2%
(Missing)31950
94.2%
2021-03-23T14:55:29.828142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lv1936
 
9.8%
council1785
 
9.1%
below1109
 
5.6%
less823
 
4.2%
than815
 
4.1%
and703
 
3.6%
in567
 
2.9%
growing554
 
2.8%
tree529
 
2.7%
to460
 
2.3%
Other values (873)10399
52.8%

Most occurring characters

ValueCountFrequency (%)
17736
15.6%
e8609
 
7.6%
n7407
 
6.5%
o6467
 
5.7%
l6257
 
5.5%
i6122
 
5.4%
a6079
 
5.3%
c5605
 
4.9%
t5479
 
4.8%
s4846
 
4.3%
Other values (66)39260
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter79442
69.8%
Space Separator17736
 
15.6%
Uppercase Letter9248
 
8.1%
Decimal Number5282
 
4.6%
Other Punctuation1680
 
1.5%
Math Symbol229
 
0.2%
Dash Punctuation228
 
0.2%
Open Punctuation9
 
< 0.1%
Close Punctuation9
 
< 0.1%
Connector Punctuation4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e8609
10.8%
n7407
9.3%
o6467
 
8.1%
l6257
 
7.9%
i6122
 
7.7%
a6079
 
7.7%
c5605
 
7.1%
t5479
 
6.9%
s4846
 
6.1%
r4261
 
5.4%
Other values (16)18310
23.0%
ValueCountFrequency (%)
V2327
25.2%
L2271
24.6%
C1941
21.0%
H383
 
4.1%
S358
 
3.9%
M352
 
3.8%
E340
 
3.7%
I201
 
2.2%
P191
 
2.1%
A185
 
2.0%
Other values (15)699
 
7.6%
ValueCountFrequency (%)
11307
24.7%
01180
22.3%
5741
14.0%
3407
 
7.7%
6368
 
7.0%
2323
 
6.1%
8308
 
5.8%
4223
 
4.2%
7222
 
4.2%
9203
 
3.8%
ValueCountFrequency (%)
.1249
74.3%
&188
 
11.2%
,77
 
4.6%
#73
 
4.3%
/50
 
3.0%
:33
 
2.0%
'6
 
0.4%
"2
 
0.1%
*2
 
0.1%
ValueCountFrequency (%)
17736
100.0%
ValueCountFrequency (%)
<229
100.0%
ValueCountFrequency (%)
-228
100.0%
ValueCountFrequency (%)
_4
100.0%
ValueCountFrequency (%)
(9
100.0%
ValueCountFrequency (%)
)9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin88690
77.9%
Common25177
 
22.1%

Most frequent character per script

ValueCountFrequency (%)
e8609
 
9.7%
n7407
 
8.4%
o6467
 
7.3%
l6257
 
7.1%
i6122
 
6.9%
a6079
 
6.9%
c5605
 
6.3%
t5479
 
6.2%
s4846
 
5.5%
r4261
 
4.8%
Other values (41)27558
31.1%
ValueCountFrequency (%)
17736
70.4%
11307
 
5.2%
.1249
 
5.0%
01180
 
4.7%
5741
 
2.9%
3407
 
1.6%
6368
 
1.5%
2323
 
1.3%
8308
 
1.2%
<229
 
0.9%
Other values (15)1329
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII113867
100.0%

Most frequent character per block

ValueCountFrequency (%)
17736
15.6%
e8609
 
7.6%
n7407
 
6.5%
o6467
 
5.7%
l6257
 
5.5%
i6122
 
5.4%
a6079
 
5.3%
c5605
 
4.9%
t5479
 
4.8%
s4846
 
4.3%
Other values (66)39260
34.5%

CouncilClearanceRange
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing32085
Missing (%)94.6%
Memory size66.4 KiB
True
 
1727
False
 
105
(Missing)
32085 
ValueCountFrequency (%)
True1727
 
5.1%
False105
 
0.3%
(Missing)32085
94.6%
2021-03-23T14:55:29.947545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

CouncilClearanceSpace
Categorical

HIGH CARDINALITY
MISSING

Distinct113
Distinct (%)6.1%
Missing32057
Missing (%)94.5%
Memory size265.1 KiB
0
497 
700
 
61
800
 
57
400
 
55
100
 
54
Other values (108)
1136 

Length

Max length6
Median length3
Mean length2.444086022
Min length1

Characters and Unicode

Total characters4546
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)1.2%

Sample

1st row0
2nd row800
3rd row450
4th row360
5th row300
ValueCountFrequency (%)
0497
 
1.5%
70061
 
0.2%
80057
 
0.2%
40055
 
0.2%
10054
 
0.2%
60054
 
0.2%
45040
 
0.1%
55039
 
0.1%
30037
 
0.1%
75036
 
0.1%
Other values (103)930
 
2.7%
(Missing)32057
94.5%
2021-03-23T14:55:30.212535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0498
26.8%
70061
 
3.3%
80057
 
3.1%
40055
 
3.0%
10054
 
2.9%
60054
 
2.9%
45040
 
2.1%
55039
 
2.1%
30037
 
2.0%
75036
 
1.9%
Other values (103)930
50.0%

Most occurring characters

ValueCountFrequency (%)
02231
49.1%
5433
 
9.5%
7320
 
7.0%
8299
 
6.6%
6279
 
6.1%
4265
 
5.8%
3240
 
5.3%
2198
 
4.4%
1188
 
4.1%
986
 
1.9%
Other values (6)7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4539
99.8%
Lowercase Letter6
 
0.1%
Space Separator1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
02231
49.2%
5433
 
9.5%
7320
 
7.1%
8299
 
6.6%
6279
 
6.1%
4265
 
5.8%
3240
 
5.3%
2198
 
4.4%
1188
 
4.1%
986
 
1.9%
ValueCountFrequency (%)
e2
33.3%
t1
16.7%
r1
16.7%
i1
16.7%
l1
16.7%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4540
99.9%
Latin6
 
0.1%

Most frequent character per script

ValueCountFrequency (%)
02231
49.1%
5433
 
9.5%
7320
 
7.0%
8299
 
6.6%
6279
 
6.1%
4265
 
5.8%
3240
 
5.3%
2198
 
4.4%
1188
 
4.1%
986
 
1.9%
ValueCountFrequency (%)
e2
33.3%
t1
16.7%
r1
16.7%
i1
16.7%
l1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4546
100.0%

Most frequent character per block

ValueCountFrequency (%)
02231
49.1%
5433
 
9.5%
7320
 
7.0%
8299
 
6.6%
6279
 
6.1%
4265
 
5.8%
3240
 
5.3%
2198
 
4.4%
1188
 
4.1%
986
 
1.9%
Other values (6)7
 
0.2%

CouncilElectricLineContact
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing32047
Missing (%)94.5%
Memory size265.1 KiB
no_signs_of_contact
1356 
signs_of_contact_or through
514 

Length

Max length27
Median length19
Mean length21.19893048
Min length19

Characters and Unicode

Total characters39642
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsigns_of_contact_or through
2nd rowno_signs_of_contact
3rd rowno_signs_of_contact
4th rowno_signs_of_contact
5th rowno_signs_of_contact
ValueCountFrequency (%)
no_signs_of_contact1356
 
4.0%
signs_of_contact_or through514
 
1.5%
(Missing)32047
94.5%
2021-03-23T14:55:30.474228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:30.567445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
no_signs_of_contact1356
56.9%
signs_of_contact_or514
 
21.6%
through514
 
21.6%

Most occurring characters

ValueCountFrequency (%)
o6124
15.4%
_5610
14.2%
n5096
12.9%
t4254
10.7%
s3740
9.4%
c3740
9.4%
g2384
 
6.0%
i1870
 
4.7%
f1870
 
4.7%
a1870
 
4.7%
Other values (4)3084
7.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter33518
84.6%
Connector Punctuation5610
 
14.2%
Space Separator514
 
1.3%

Most frequent character per category

ValueCountFrequency (%)
o6124
18.3%
n5096
15.2%
t4254
12.7%
s3740
11.2%
c3740
11.2%
g2384
 
7.1%
i1870
 
5.6%
f1870
 
5.6%
a1870
 
5.6%
r1028
 
3.1%
Other values (2)1542
 
4.6%
ValueCountFrequency (%)
_5610
100.0%
ValueCountFrequency (%)
514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin33518
84.6%
Common6124
 
15.4%

Most frequent character per script

ValueCountFrequency (%)
o6124
18.3%
n5096
15.2%
t4254
12.7%
s3740
11.2%
c3740
11.2%
g2384
 
7.1%
i1870
 
5.6%
f1870
 
5.6%
a1870
 
5.6%
r1028
 
3.1%
Other values (2)1542
 
4.6%
ValueCountFrequency (%)
_5610
91.6%
514
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII39642
100.0%

Most frequent character per block

ValueCountFrequency (%)
o6124
15.4%
_5610
14.2%
n5096
12.9%
t4254
10.7%
s3740
9.4%
c3740
9.4%
g2384
 
6.0%
i1870
 
4.7%
f1870
 
4.7%
a1870
 
4.7%
Other values (4)3084
7.8%

CouncilGenus
Categorical

HIGH CARDINALITY
MISSING

Distinct226
Distinct (%)11.5%
Missing31950
Missing (%)94.2%
Memory size265.1 KiB
gum
334 
mela._call.
324 
lophostemon
133 
other
132 
platanus
121 
Other values (221)
923 

Length

Max length33
Median length8
Mean length9.066090493
Min length3

Characters and Unicode

Total characters17833
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)6.7%

Sample

1st rowquercus,mela._call.,ulmus
2nd rowlophostemon,liquidambar,grevillea
3rd rowquercus,lophostemon
4th rowmela._call.
5th rowmela._call.
ValueCountFrequency (%)
gum334
 
1.0%
mela._call.324
 
1.0%
lophostemon133
 
0.4%
other132
 
0.4%
platanus121
 
0.4%
quercus94
 
0.3%
pyrus75
 
0.2%
fraxinus73
 
0.2%
melia59
 
0.2%
ulmus58
 
0.2%
Other values (216)564
 
1.7%
(Missing)31950
94.2%
2021-03-23T14:55:30.879326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gum334
17.0%
mela._call324
16.5%
lophostemon133
 
6.8%
other132
 
6.7%
platanus121
 
6.2%
quercus94
 
4.8%
pyrus75
 
3.8%
fraxinus73
 
3.7%
melia59
 
3.0%
ulmus58
 
2.9%
Other values (216)564
28.7%

Most occurring characters

ValueCountFrequency (%)
l2180
12.2%
a2011
 
11.3%
u1419
 
8.0%
m1337
 
7.5%
e1268
 
7.1%
s1002
 
5.6%
.952
 
5.3%
o944
 
5.3%
c859
 
4.8%
r777
 
4.4%
Other values (16)5084
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15927
89.3%
Other Punctuation1394
 
7.8%
Connector Punctuation512
 
2.9%

Most frequent character per category

ValueCountFrequency (%)
l2180
13.7%
a2011
12.6%
u1419
8.9%
m1337
 
8.4%
e1268
 
8.0%
s1002
 
6.3%
o944
 
5.9%
c859
 
5.4%
r777
 
4.9%
n653
 
4.1%
Other values (13)3477
21.8%
ValueCountFrequency (%)
.952
68.3%
,442
31.7%
ValueCountFrequency (%)
_512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15927
89.3%
Common1906
 
10.7%

Most frequent character per script

ValueCountFrequency (%)
l2180
13.7%
a2011
12.6%
u1419
8.9%
m1337
 
8.4%
e1268
 
8.0%
s1002
 
6.3%
o944
 
5.9%
c859
 
5.4%
r777
 
4.9%
n653
 
4.1%
Other values (13)3477
21.8%
ValueCountFrequency (%)
.952
49.9%
_512
26.9%
,442
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII17833
100.0%

Most frequent character per block

ValueCountFrequency (%)
l2180
12.2%
a2011
 
11.3%
u1419
 
8.0%
m1337
 
7.5%
e1268
 
7.1%
s1002
 
5.6%
.952
 
5.3%
o944
 
5.3%
c859
 
4.8%
r777
 
4.4%
Other values (16)5084
28.5%

CouncilNonComplianceCode
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31950
Missing (%)94.2%
Memory size265.1 KiB
HRNC
1060 
NC
907 

Length

Max length4
Median length4
Mean length3.077783427
Min length2

Characters and Unicode

Total characters6054
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHRNC
2nd rowNC
3rd rowHRNC
4th rowHRNC
5th rowHRNC
ValueCountFrequency (%)
HRNC1060
 
3.1%
NC907
 
2.7%
(Missing)31950
94.2%
2021-03-23T14:55:31.300757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:31.391896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hrnc1060
53.9%
nc907
46.1%

Most occurring characters

ValueCountFrequency (%)
N1967
32.5%
C1967
32.5%
H1060
17.5%
R1060
17.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6054
100.0%

Most frequent character per category

ValueCountFrequency (%)
N1967
32.5%
C1967
32.5%
H1060
17.5%
R1060
17.5%

Most occurring scripts

ValueCountFrequency (%)
Latin6054
100.0%

Most frequent character per script

ValueCountFrequency (%)
N1967
32.5%
C1967
32.5%
H1060
17.5%
R1060
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6054
100.0%

Most frequent character per block

ValueCountFrequency (%)
N1967
32.5%
C1967
32.5%
H1060
17.5%
R1060
17.5%

CouncilOtherInfrastructurePresent
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31950
Missing (%)94.2%
Memory size66.4 KiB
False
 
1942
True
 
25
(Missing)
31950 
ValueCountFrequency (%)
False1942
 
5.7%
True25
 
0.1%
(Missing)31950
94.2%
2021-03-23T14:55:31.442581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

CouncilSingleOrMultipleTrees
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31950
Missing (%)94.2%
Memory size265.1 KiB
single
1149 
multiple
818 

Length

Max length8
Median length6
Mean length6.831723437
Min length6

Characters and Unicode

Total characters13438
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmultiple
2nd rowsingle
3rd rowmultiple
4th rowsingle
5th rowsingle
ValueCountFrequency (%)
single1149
 
3.4%
multiple818
 
2.4%
(Missing)31950
94.2%
2021-03-23T14:55:31.675410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:31.766513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
single1149
58.4%
multiple818
41.6%

Most occurring characters

ValueCountFrequency (%)
l2785
20.7%
i1967
14.6%
e1967
14.6%
s1149
8.6%
n1149
8.6%
g1149
8.6%
m818
 
6.1%
u818
 
6.1%
t818
 
6.1%
p818
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13438
100.0%

Most frequent character per category

ValueCountFrequency (%)
l2785
20.7%
i1967
14.6%
e1967
14.6%
s1149
8.6%
n1149
8.6%
g1149
8.6%
m818
 
6.1%
u818
 
6.1%
t818
 
6.1%
p818
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin13438
100.0%

Most frequent character per script

ValueCountFrequency (%)
l2785
20.7%
i1967
14.6%
e1967
14.6%
s1149
8.6%
n1149
8.6%
g1149
8.6%
m818
 
6.1%
u818
 
6.1%
t818
 
6.1%
p818
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13438
100.0%

Most frequent character per block

ValueCountFrequency (%)
l2785
20.7%
i1967
14.6%
e1967
14.6%
s1149
8.6%
n1149
8.6%
g1149
8.6%
m818
 
6.1%
u818
 
6.1%
t818
 
6.1%
p818
 
6.1%

CouncilSpanVoltages
Categorical

HIGH CORRELATION
MISSING

Distinct19
Distinct (%)1.0%
Missing31950
Missing (%)94.2%
Memory size265.1 KiB
LV
1502 
LV,HV
 
144
HV
 
100
HV,LV
 
73
66
 
40
Other values (14)
 
108

Length

Max length15
Median length2
Mean length2.775292323
Min length2

Characters and Unicode

Total characters5459
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowLV
2nd rowLV
3rd rowLV
4th rowLV
5th rowLV
ValueCountFrequency (%)
LV1502
 
4.4%
LV,HV144
 
0.4%
HV100
 
0.3%
HV,LV73
 
0.2%
6640
 
0.1%
insulated40
 
0.1%
LV,insulated28
 
0.1%
HV,insulated8
 
< 0.1%
LV,666
 
< 0.1%
HV,665
 
< 0.1%
Other values (9)21
 
0.1%
(Missing)31950
94.2%
2021-03-23T14:55:32.029671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lv1502
76.4%
lv,hv144
 
7.3%
hv100
 
5.1%
hv,lv73
 
3.7%
6640
 
2.0%
insulated40
 
2.0%
lv,insulated28
 
1.4%
hv,insulated8
 
0.4%
lv,666
 
0.3%
hv,665
 
0.3%
Other values (9)21
 
1.1%

Most occurring characters

ValueCountFrequency (%)
V2114
38.7%
L1768
32.4%
H346
 
6.3%
,296
 
5.4%
6116
 
2.1%
i91
 
1.7%
n91
 
1.7%
s91
 
1.7%
u91
 
1.7%
l91
 
1.7%
Other values (4)364
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4228
77.5%
Lowercase Letter819
 
15.0%
Other Punctuation296
 
5.4%
Decimal Number116
 
2.1%

Most frequent character per category

ValueCountFrequency (%)
i91
11.1%
n91
11.1%
s91
11.1%
u91
11.1%
l91
11.1%
a91
11.1%
t91
11.1%
e91
11.1%
d91
11.1%
ValueCountFrequency (%)
V2114
50.0%
L1768
41.8%
H346
 
8.2%
ValueCountFrequency (%)
,296
100.0%
ValueCountFrequency (%)
6116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5047
92.5%
Common412
 
7.5%

Most frequent character per script

ValueCountFrequency (%)
V2114
41.9%
L1768
35.0%
H346
 
6.9%
i91
 
1.8%
n91
 
1.8%
s91
 
1.8%
u91
 
1.8%
l91
 
1.8%
a91
 
1.8%
t91
 
1.8%
Other values (2)182
 
3.6%
ValueCountFrequency (%)
,296
71.8%
6116
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5459
100.0%

Most frequent character per block

ValueCountFrequency (%)
V2114
38.7%
L1768
32.4%
H346
 
6.3%
,296
 
5.4%
6116
 
2.1%
i91
 
1.7%
n91
 
1.7%
s91
 
1.7%
u91
 
1.7%
l91
 
1.7%
Other values (4)364
 
6.7%

ElectricLineContact
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing32166
Missing (%)94.8%
Memory size265.1 KiB
no_signs_of_contact
1227 
signs_of_contact_or through
524 

Length

Max length27
Median length19
Mean length21.39406054
Min length19

Characters and Unicode

Total characters37461
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno_signs_of_contact
2nd rowno_signs_of_contact
3rd rowno_signs_of_contact
4th rowsigns_of_contact_or through
5th rowno_signs_of_contact
ValueCountFrequency (%)
no_signs_of_contact1227
 
3.6%
signs_of_contact_or through524
 
1.5%
(Missing)32166
94.8%
2021-03-23T14:55:32.303022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:32.394109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
no_signs_of_contact1227
53.9%
signs_of_contact_or524
23.0%
through524
23.0%

Most occurring characters

ValueCountFrequency (%)
o5777
15.4%
_5253
14.0%
n4729
12.6%
t4026
10.7%
s3502
9.3%
c3502
9.3%
g2275
 
6.1%
i1751
 
4.7%
f1751
 
4.7%
a1751
 
4.7%
Other values (4)3144
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31684
84.6%
Connector Punctuation5253
 
14.0%
Space Separator524
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
o5777
18.2%
n4729
14.9%
t4026
12.7%
s3502
11.1%
c3502
11.1%
g2275
 
7.2%
i1751
 
5.5%
f1751
 
5.5%
a1751
 
5.5%
r1048
 
3.3%
Other values (2)1572
 
5.0%
ValueCountFrequency (%)
_5253
100.0%
ValueCountFrequency (%)
524
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31684
84.6%
Common5777
 
15.4%

Most frequent character per script

ValueCountFrequency (%)
o5777
18.2%
n4729
14.9%
t4026
12.7%
s3502
11.1%
c3502
11.1%
g2275
 
7.2%
i1751
 
5.5%
f1751
 
5.5%
a1751
 
5.5%
r1048
 
3.3%
Other values (2)1572
 
5.0%
ValueCountFrequency (%)
_5253
90.9%
524
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII37461
100.0%

Most frequent character per block

ValueCountFrequency (%)
o5777
15.4%
_5253
14.0%
n4729
12.6%
t4026
10.7%
s3502
9.3%
c3502
9.3%
g2275
 
6.1%
i1751
 
4.7%
f1751
 
4.7%
a1751
 
4.7%
Other values (4)3144
8.4%

ElectricalAssets
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct83
Distinct (%)0.3%
Missing3868
Missing (%)11.4%
Memory size265.1 KiB
HV
8502 
HV,LV
6680 
LV
6364 
SWER
1872 
HV,transformer
1243 
Other values (78)
5388 

Length

Max length27
Median length2
Mean length4.628406935
Min length2

Characters and Unicode

Total characters139079
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.1%

Sample

1st rowHV
2nd rowHV
3rd rowHV,transformer
4th rowLV
5th rowSWER,transformer
ValueCountFrequency (%)
HV8502
25.1%
HV,LV6680
19.7%
LV6364
18.8%
SWER1872
 
5.5%
HV,transformer1243
 
3.7%
LV,HV945
 
2.8%
insulated740
 
2.2%
66,HV584
 
1.7%
HV,insulated581
 
1.7%
HV,LV,transformer569
 
1.7%
Other values (73)1969
 
5.8%
(Missing)3868
11.4%
2021-03-23T14:55:32.677489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hv8502
28.3%
hv,lv6680
22.2%
lv6364
21.2%
swer1872
 
6.2%
hv,transformer1243
 
4.1%
lv,hv945
 
3.1%
insulated740
 
2.5%
66,hv584
 
1.9%
hv,insulated581
 
1.9%
hv,lv,transformer569
 
1.9%
Other values (73)1969
 
6.6%

Most occurring characters

ValueCountFrequency (%)
V35277
25.4%
H19882
14.3%
L15395
11.1%
,13317
 
9.6%
r7532
 
5.4%
s4217
 
3.0%
e4216
 
3.0%
t4215
 
3.0%
a4213
 
3.0%
n4212
 
3.0%
Other values (14)26603
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter79562
57.2%
Lowercase Letter42956
30.9%
Other Punctuation13317
 
9.6%
Decimal Number3244
 
2.3%

Most frequent character per category

ValueCountFrequency (%)
r7532
17.5%
s4217
9.8%
e4216
9.8%
t4215
9.8%
a4213
9.8%
n4212
9.8%
o2512
 
5.8%
f2510
 
5.8%
m2510
 
5.8%
l1706
 
4.0%
Other values (5)5113
11.9%
ValueCountFrequency (%)
V35277
44.3%
H19882
25.0%
L15395
19.3%
S2252
 
2.8%
W2252
 
2.8%
E2252
 
2.8%
R2252
 
2.8%
ValueCountFrequency (%)
,13317
100.0%
ValueCountFrequency (%)
63244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin122518
88.1%
Common16561
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
V35277
28.8%
H19882
16.2%
L15395
12.6%
r7532
 
6.1%
s4217
 
3.4%
e4216
 
3.4%
t4215
 
3.4%
a4213
 
3.4%
n4212
 
3.4%
o2512
 
2.1%
Other values (12)20847
17.0%
ValueCountFrequency (%)
,13317
80.4%
63244
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII139079
100.0%

Most frequent character per block

ValueCountFrequency (%)
V35277
25.4%
H19882
14.3%
L15395
11.1%
,13317
 
9.6%
r7532
 
5.4%
s4217
 
3.0%
e4216
 
3.0%
t4215
 
3.0%
a4213
 
3.0%
n4212
 
3.0%
Other values (14)26603
19.1%

FinancialYear
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
2018-19
19139 
2019-20
10403 
2017-18
4375 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters237419
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-19
2nd row2018-19
3rd row2018-19
4th row2018-19
5th row2018-19
ValueCountFrequency (%)
2018-1919139
56.4%
2019-2010403
30.7%
2017-184375
 
12.9%
2021-03-23T14:55:32.952814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:33.033755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-1919139
56.4%
2019-2010403
30.7%
2017-184375
 
12.9%

Most occurring characters

ValueCountFrequency (%)
157431
24.2%
244320
18.7%
044320
18.7%
-33917
14.3%
929542
12.4%
823514
9.9%
74375
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number203502
85.7%
Dash Punctuation33917
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
157431
28.2%
244320
21.8%
044320
21.8%
929542
14.5%
823514
11.6%
74375
 
2.1%
ValueCountFrequency (%)
-33917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common237419
100.0%

Most frequent character per script

ValueCountFrequency (%)
157431
24.2%
244320
18.7%
044320
18.7%
-33917
14.3%
929542
12.4%
823514
9.9%
74375
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII237419
100.0%

Most frequent character per block

ValueCountFrequency (%)
157431
24.2%
244320
18.7%
044320
18.7%
-33917
14.3%
929542
12.4%
823514
9.9%
74375
 
1.8%

FireHazardDeclaredStatus
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
HBRA_non
17917 
LBRA_non
8539 
LBRA_dec
6986 
HBRA_dec
 
475

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters271336
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHBRA_non
2nd rowHBRA_non
3rd rowHBRA_non
4th rowHBRA_non
5th rowHBRA_non
ValueCountFrequency (%)
HBRA_non17917
52.8%
LBRA_non8539
25.2%
LBRA_dec6986
 
20.6%
HBRA_dec475
 
1.4%
2021-03-23T14:55:33.289077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:33.370034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hbra_non17917
52.8%
lbra_non8539
25.2%
lbra_dec6986
 
20.6%
hbra_dec475
 
1.4%

Most occurring characters

ValueCountFrequency (%)
n52912
19.5%
B33917
12.5%
R33917
12.5%
A33917
12.5%
_33917
12.5%
o26456
9.8%
H18392
 
6.8%
L15525
 
5.7%
d7461
 
2.7%
e7461
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter135668
50.0%
Lowercase Letter101751
37.5%
Connector Punctuation33917
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
B33917
25.0%
R33917
25.0%
A33917
25.0%
H18392
13.6%
L15525
11.4%
ValueCountFrequency (%)
n52912
52.0%
o26456
26.0%
d7461
 
7.3%
e7461
 
7.3%
c7461
 
7.3%
ValueCountFrequency (%)
_33917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin237419
87.5%
Common33917
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
n52912
22.3%
B33917
14.3%
R33917
14.3%
A33917
14.3%
o26456
11.1%
H18392
 
7.7%
L15525
 
6.5%
d7461
 
3.1%
e7461
 
3.1%
c7461
 
3.1%
ValueCountFrequency (%)
_33917
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII271336
100.0%

Most frequent character per block

ValueCountFrequency (%)
n52912
19.5%
B33917
12.5%
R33917
12.5%
A33917
12.5%
_33917
12.5%
o26456
9.8%
H18392
 
6.8%
L15525
 
5.7%
d7461
 
2.7%
e7461
 
2.7%

Genus
Categorical

HIGH CARDINALITY
MISSING

Distinct294
Distinct (%)13.4%
Missing31728
Missing (%)93.5%
Memory size265.1 KiB
gum
623 
acacia
137 
other
119 
mela._call.
 
106
conifers
 
101
Other values (289)
1103 

Length

Max length45
Median length6
Mean length8.061671996
Min length3

Characters and Unicode

Total characters17647
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)8.8%

Sample

1st rowgum
2nd rowjacaranda
3rd rowgum
4th rowgum
5th rowconifers
ValueCountFrequency (%)
gum623
 
1.8%
acacia137
 
0.4%
other119
 
0.4%
mela._call.106
 
0.3%
conifers101
 
0.3%
fraxinus98
 
0.3%
palm55
 
0.2%
acacia,gum54
 
0.2%
casuarina51
 
0.2%
quercus47
 
0.1%
Other values (284)798
 
2.4%
(Missing)31728
93.5%
2021-03-23T14:55:33.683948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gum623
28.5%
acacia137
 
6.3%
other119
 
5.4%
mela._call106
 
4.8%
conifers101
 
4.6%
fraxinus98
 
4.5%
palm55
 
2.5%
acacia,gum54
 
2.5%
casuarina51
 
2.3%
quercus47
 
2.1%
Other values (284)798
36.5%

Most occurring characters

ValueCountFrequency (%)
a2316
13.1%
u1703
 
9.7%
c1375
 
7.8%
m1304
 
7.4%
i1138
 
6.4%
l1100
 
6.2%
s1021
 
5.8%
e972
 
5.5%
g969
 
5.5%
r935
 
5.3%
Other values (16)4814
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16131
91.4%
Other Punctuation1223
 
6.9%
Connector Punctuation293
 
1.7%

Most frequent character per category

ValueCountFrequency (%)
a2316
14.4%
u1703
10.6%
c1375
8.5%
m1304
 
8.1%
i1138
 
7.1%
l1100
 
6.8%
s1021
 
6.3%
e972
 
6.0%
g969
 
6.0%
r935
 
5.8%
Other values (13)3298
20.4%
ValueCountFrequency (%)
,703
57.5%
.520
42.5%
ValueCountFrequency (%)
_293
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16131
91.4%
Common1516
 
8.6%

Most frequent character per script

ValueCountFrequency (%)
a2316
14.4%
u1703
10.6%
c1375
8.5%
m1304
 
8.1%
i1138
 
7.1%
l1100
 
6.8%
s1021
 
6.3%
e972
 
6.0%
g969
 
6.0%
r935
 
5.8%
Other values (13)3298
20.4%
ValueCountFrequency (%)
,703
46.4%
.520
34.3%
_293
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII17647
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2316
13.1%
u1703
 
9.7%
c1375
 
7.8%
m1304
 
7.4%
i1138
 
6.4%
l1100
 
6.2%
s1021
 
5.8%
e972
 
5.5%
g969
 
5.5%
r935
 
5.3%
Other values (16)4814
27.3%

HazardAssessment
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
True
33900 
False
 
17
ValueCountFrequency (%)
True33900
99.9%
False17
 
0.1%
2021-03-23T14:55:33.785147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

InspectionDatetime
Categorical

HIGH CARDINALITY

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
36:00.0
 
1532
20:00.0
 
621
58:00.0
 
619
02:00.0
 
614
47:00.0
 
613
Other values (55)
29918 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters237419
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22:00.0
2nd row23:00.0
3rd row24:00.0
4th row26:00.0
5th row29:00.0
ValueCountFrequency (%)
36:00.01532
 
4.5%
20:00.0621
 
1.8%
58:00.0619
 
1.8%
02:00.0614
 
1.8%
47:00.0613
 
1.8%
01:00.0607
 
1.8%
44:00.0593
 
1.7%
03:00.0592
 
1.7%
45:00.0590
 
1.7%
46:00.0589
 
1.7%
Other values (50)26947
79.4%
2021-03-23T14:55:34.048144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36:00.01532
 
4.5%
20:00.0621
 
1.8%
58:00.0619
 
1.8%
02:00.0614
 
1.8%
47:00.0613
 
1.8%
01:00.0607
 
1.8%
44:00.0593
 
1.7%
03:00.0592
 
1.7%
45:00.0590
 
1.7%
46:00.0589
 
1.7%
Other values (50)26947
79.4%

Most occurring characters

ValueCountFrequency (%)
0110656
46.6%
:33917
 
14.3%
.33917
 
14.3%
39710
 
4.1%
48892
 
3.7%
18788
 
3.7%
28757
 
3.7%
58623
 
3.6%
64219
 
1.8%
93338
 
1.4%
Other values (2)6602
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169585
71.4%
Other Punctuation67834
 
28.6%

Most frequent character per category

ValueCountFrequency (%)
0110656
65.3%
39710
 
5.7%
48892
 
5.2%
18788
 
5.2%
28757
 
5.2%
58623
 
5.1%
64219
 
2.5%
93338
 
2.0%
73330
 
2.0%
83272
 
1.9%
ValueCountFrequency (%)
:33917
50.0%
.33917
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common237419
100.0%

Most frequent character per script

ValueCountFrequency (%)
0110656
46.6%
:33917
 
14.3%
.33917
 
14.3%
39710
 
4.1%
48892
 
3.7%
18788
 
3.7%
28757
 
3.7%
58623
 
3.6%
64219
 
1.8%
93338
 
1.4%
Other values (2)6602
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII237419
100.0%

Most frequent character per block

ValueCountFrequency (%)
0110656
46.6%
:33917
 
14.3%
.33917
 
14.3%
39710
 
4.1%
48892
 
3.7%
18788
 
3.7%
28757
 
3.7%
58623
 
3.6%
64219
 
1.8%
93338
 
1.4%
Other values (2)6602
 
2.8%

Lat
Real number (ℝ)

Distinct33307
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-37.5327025
Minimum-38.88881224
Maximum-34.11878443
Zeros0
Zeros (%)0.0%
Memory size265.1 KiB
2021-03-23T14:55:34.189818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-38.88881224
5-th percentile-38.41628631
Q1-38.18845566
median-37.75069071
Q3-37.20013076
95-th percentile-35.89627019
Maximum-34.11878443
Range4.77002781
Interquartile range (IQR)0.9883249

Descriptive statistics

Standard deviation0.8789582125
Coefficient of variation (CV)-0.0234184632
Kurtosis2.198141174
Mean-37.5327025
Median Absolute Deviation (MAD)0.45574321
Skewness1.459891551
Sum-1272996.671
Variance0.7725675393
MonotocityNot monotonic
2021-03-23T14:55:34.349183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-37.3995037417
 
0.1%
-37.4162392616
 
< 0.1%
-38.0928836814
 
< 0.1%
-37.4558926512
 
< 0.1%
-37.5027703611
 
< 0.1%
-38.0203655210
 
< 0.1%
-37.4312319510
 
< 0.1%
-38.340954949
 
< 0.1%
-37.453222649
 
< 0.1%
-38.364938358
 
< 0.1%
Other values (33297)33801
99.7%
ValueCountFrequency (%)
-38.888812241
< 0.1%
-38.888027951
< 0.1%
-38.886348791
< 0.1%
-38.884821861
< 0.1%
-38.882970581
< 0.1%
ValueCountFrequency (%)
-34.118784431
< 0.1%
-34.119307391
< 0.1%
-34.119918421
< 0.1%
-34.120528551
< 0.1%
-34.120941351
< 0.1%

Locality
Categorical

HIGH CARDINALITY

Distinct1388
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
RYE
 
266
MOUNT MARTHA
 
263
MOUNT ELIZA
 
253
LANGWARRIN
 
249
PEARCEDALE
 
224
Other values (1383)
32662 

Length

Max length21
Median length9
Mean length9.41209423
Min length3

Characters and Unicode

Total characters319230
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.1%

Sample

1st rowYARRABERB
2nd rowYARRABERB
3rd rowYARRABERB
4th rowYARRABERB
5th rowWHIPSTICK
ValueCountFrequency (%)
RYE266
 
0.8%
MOUNT MARTHA263
 
0.8%
MOUNT ELIZA253
 
0.7%
LANGWARRIN249
 
0.7%
PEARCEDALE224
 
0.7%
BLAIRGOWRIE220
 
0.6%
CRANBOURNE SOUTH213
 
0.6%
MAIN RIDGE212
 
0.6%
HASTINGS202
 
0.6%
RIDDELLS CREEK199
 
0.6%
Other values (1378)31616
93.2%
2021-03-23T14:55:34.693425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south1065
 
2.4%
mount910
 
2.0%
north857
 
1.9%
creek622
 
1.4%
east539
 
1.2%
hill526
 
1.2%
west484
 
1.1%
beach474
 
1.1%
cranbourne423
 
0.9%
langwarrin384
 
0.9%
Other values (1300)38672
86.0%

Most occurring characters

ValueCountFrequency (%)
E30920
 
9.7%
A30745
 
9.6%
R28658
 
9.0%
O27546
 
8.6%
N24704
 
7.7%
L21097
 
6.6%
T16965
 
5.3%
I15250
 
4.8%
S14593
 
4.6%
11039
 
3.5%
Other values (17)97713
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter308191
96.5%
Space Separator11039
 
3.5%

Most frequent character per category

ValueCountFrequency (%)
E30920
 
10.0%
A30745
 
10.0%
R28658
 
9.3%
O27546
 
8.9%
N24704
 
8.0%
L21097
 
6.8%
T16965
 
5.5%
I15250
 
4.9%
S14593
 
4.7%
D10868
 
3.5%
Other values (16)86845
28.2%
ValueCountFrequency (%)
11039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin308191
96.5%
Common11039
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
E30920
 
10.0%
A30745
 
10.0%
R28658
 
9.3%
O27546
 
8.9%
N24704
 
8.0%
L21097
 
6.8%
T16965
 
5.5%
I15250
 
4.9%
S14593
 
4.7%
D10868
 
3.5%
Other values (16)86845
28.2%
ValueCountFrequency (%)
11039
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII319230
100.0%

Most frequent character per block

ValueCountFrequency (%)
E30920
 
9.7%
A30745
 
9.6%
R28658
 
9.0%
O27546
 
8.6%
N24704
 
7.7%
L21097
 
6.6%
T16965
 
5.3%
I15250
 
4.8%
S14593
 
4.6%
11039
 
3.5%
Other values (17)97713
30.6%

LocationFeeder
Categorical

HIGH CARDINALITY
MISSING

Distinct660
Distinct (%)2.2%
Missing3913
Missing (%)11.5%
Memory size265.1 KiB
XX
6256 
EHK024
 
504
BAS011
 
491
MTN32
 
380
CHM011
 
301
Other values (655)
22072 

Length

Max length14
Median length5
Mean length4.622816958
Min length1

Characters and Unicode

Total characters138703
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)0.1%

Sample

1st rowEHK024
2nd rowEHK024
3rd rowEHK024
4th rowEHK024
5th rowEHK023
ValueCountFrequency (%)
XX6256
 
18.4%
EHK024504
 
1.5%
BAS011491
 
1.4%
MTN32380
 
1.1%
CHM011301
 
0.9%
SLE31300
 
0.9%
RUBA12290
 
0.9%
BAN006280
 
0.8%
SMR5274
 
0.8%
LWN21270
 
0.8%
Other values (650)20658
60.9%
(Missing)3913
 
11.5%
2021-03-23T14:55:35.009122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xx6256
 
20.9%
ehk024504
 
1.7%
bas011491
 
1.6%
mtn32380
 
1.3%
chm011301
 
1.0%
sle31300
 
1.0%
ruba12290
 
1.0%
ban006280
 
0.9%
smr5274
 
0.9%
cha006270
 
0.9%
Other values (649)20658
68.9%

Most occurring characters

ValueCountFrequency (%)
015847
 
11.4%
112813
 
9.2%
X12518
 
9.0%
210589
 
7.6%
N6973
 
5.0%
36868
 
5.0%
T5902
 
4.3%
S5794
 
4.2%
B5785
 
4.2%
M5248
 
3.8%
Other values (27)50366
36.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter84219
60.7%
Decimal Number54270
39.1%
Dash Punctuation196
 
0.1%
Connector Punctuation15
 
< 0.1%
Close Punctuation3
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
X12518
14.9%
N6973
 
8.3%
T5902
 
7.0%
S5794
 
6.9%
B5785
 
6.9%
M5248
 
6.2%
H4982
 
5.9%
D4982
 
5.9%
L4919
 
5.8%
R3742
 
4.4%
Other values (14)23374
27.8%
ValueCountFrequency (%)
015847
29.2%
112813
23.6%
210589
19.5%
36868
12.7%
44109
 
7.6%
51436
 
2.6%
61339
 
2.5%
8580
 
1.1%
7434
 
0.8%
9255
 
0.5%
ValueCountFrequency (%)
-196
100.0%
ValueCountFrequency (%)
_15
100.0%
ValueCountFrequency (%)
)3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84219
60.7%
Common54484
39.3%

Most frequent character per script

ValueCountFrequency (%)
X12518
14.9%
N6973
 
8.3%
T5902
 
7.0%
S5794
 
6.9%
B5785
 
6.9%
M5248
 
6.2%
H4982
 
5.9%
D4982
 
5.9%
L4919
 
5.8%
R3742
 
4.4%
Other values (14)23374
27.8%
ValueCountFrequency (%)
015847
29.1%
112813
23.5%
210589
19.4%
36868
12.6%
44109
 
7.5%
51436
 
2.6%
61339
 
2.5%
8580
 
1.1%
7434
 
0.8%
9255
 
0.5%
Other values (3)214
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII138703
100.0%

Most frequent character per block

ValueCountFrequency (%)
015847
 
11.4%
112813
 
9.2%
X12518
 
9.0%
210589
 
7.6%
N6973
 
5.0%
36868
 
5.0%
T5902
 
4.3%
S5794
 
4.2%
B5785
 
4.2%
M5248
 
3.8%
Other values (27)50366
36.3%

Long
Real number (ℝ≥0)

Distinct33271
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.8644414
Minimum140.9847279
Maximum149.7593266
Zeros0
Zeros (%)0.0%
Memory size265.1 KiB
2021-03-23T14:55:35.160896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum140.9847279
5-th percentile142.1984238
Q1144.3139639
median144.9866836
Q3145.2807474
95-th percentile147.0882227
Maximum149.7593266
Range8.7745987
Interquartile range (IQR)0.9667835

Descriptive statistics

Standard deviation1.35688096
Coefficient of variation (CV)0.009366556391
Kurtosis1.308440644
Mean144.8644414
Median Absolute Deviation (MAD)0.4559366
Skewness-0.0455895821
Sum4913367.26
Variance1.841125939
MonotocityNot monotonic
2021-03-23T14:55:35.302657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144.61341717
 
0.1%
144.590991916
 
< 0.1%
145.279554414
 
< 0.1%
144.681384212
 
< 0.1%
144.565914111
 
< 0.1%
144.571966310
 
< 0.1%
145.292799510
 
< 0.1%
144.68775479
 
< 0.1%
145.1840759
 
< 0.1%
145.198578
 
< 0.1%
Other values (33261)33801
99.7%
ValueCountFrequency (%)
140.98472791
< 0.1%
140.98573481
< 0.1%
140.98606561
< 0.1%
140.98665711
< 0.1%
140.98670671
< 0.1%
ValueCountFrequency (%)
149.75932661
< 0.1%
149.75923491
< 0.1%
149.75921861
< 0.1%
149.75857881
< 0.1%
149.75788841
< 0.1%

NetworkType
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
PC
13154 
ANS
10126 
UE
7412 
JEN
2545 
CP
 
680

Length

Max length3
Median length2
Mean length2.373588466
Min length2

Characters and Unicode

Total characters80505
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPC
2nd rowPC
3rd rowPC
4th rowPC
5th rowPC
ValueCountFrequency (%)
PC13154
38.8%
ANS10126
29.9%
UE7412
21.9%
JEN2545
 
7.5%
CP680
 
2.0%
2021-03-23T14:55:35.760247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:35.859422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
pc13154
38.8%
ans10126
29.9%
ue7412
21.9%
jen2545
 
7.5%
cp680
 
2.0%

Most occurring characters

ValueCountFrequency (%)
P13834
17.2%
C13834
17.2%
N12671
15.7%
A10126
12.6%
S10126
12.6%
E9957
12.4%
U7412
9.2%
J2545
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter80505
100.0%

Most frequent character per category

ValueCountFrequency (%)
P13834
17.2%
C13834
17.2%
N12671
15.7%
A10126
12.6%
S10126
12.6%
E9957
12.4%
U7412
9.2%
J2545
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin80505
100.0%

Most frequent character per script

ValueCountFrequency (%)
P13834
17.2%
C13834
17.2%
N12671
15.7%
A10126
12.6%
S10126
12.6%
E9957
12.4%
U7412
9.2%
J2545
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII80505
100.0%

Most frequent character per block

ValueCountFrequency (%)
P13834
17.2%
C13834
17.2%
N12671
15.7%
A10126
12.6%
S10126
12.6%
E9957
12.4%
U7412
9.2%
J2545
 
3.2%

NonComplianceCode
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31461
Missing (%)92.8%
Memory size265.1 KiB
NC
1544 
HRNC
912 

Length

Max length4
Median length2
Mean length2.74267101
Min length2

Characters and Unicode

Total characters6736
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowHRNC
3rd rowNC
4th rowHRNC
5th rowNC
ValueCountFrequency (%)
NC1544
 
4.6%
HRNC912
 
2.7%
(Missing)31461
92.8%
2021-03-23T14:55:36.125025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:36.224193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nc1544
62.9%
hrnc912
37.1%

Most occurring characters

ValueCountFrequency (%)
N2456
36.5%
C2456
36.5%
H912
 
13.5%
R912
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6736
100.0%

Most frequent character per category

ValueCountFrequency (%)
N2456
36.5%
C2456
36.5%
H912
 
13.5%
R912
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin6736
100.0%

Most frequent character per script

ValueCountFrequency (%)
N2456
36.5%
C2456
36.5%
H912
 
13.5%
R912
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6736
100.0%

Most frequent character per block

ValueCountFrequency (%)
N2456
36.5%
C2456
36.5%
H912
 
13.5%
R912
 
13.5%

NonCompliant
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3591
Missing (%)10.6%
Memory size66.4 KiB
False
26003 
True
4323 
(Missing)
3591 
ValueCountFrequency (%)
False26003
76.7%
True4323
 
12.7%
(Missing)3591
 
10.6%
2021-03-23T14:55:36.276806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

OtherInfrastructurePresent
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31722
Missing (%)93.5%
Memory size66.4 KiB
False
 
2133
True
 
62
(Missing)
31722 
ValueCountFrequency (%)
False2133
 
6.3%
True62
 
0.2%
(Missing)31722
93.5%
2021-03-23T14:55:36.337478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Postcode
Real number (ℝ≥0)

Distinct511
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3562.827314
Minimum3002
Maximum3996
Zeros0
Zeros (%)0.0%
Memory size265.1 KiB
2021-03-23T14:55:36.438594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3002
5-th percentile3067
Q13314
median3559
Q33859
95-th percentile3942
Maximum3996
Range994
Interquartile range (IQR)545

Descriptive statistics

Standard deviation300.5361143
Coefficient of variation (CV)0.08435326437
Kurtosis-1.280053743
Mean3562.827314
Median Absolute Deviation (MAD)291
Skewness-0.2040482715
Sum120840414
Variance90321.956
MonotocityNot monotonic
2021-03-23T14:55:36.588280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3851879
 
2.6%
3977572
 
1.7%
3939558
 
1.6%
3941432
 
1.3%
3936388
 
1.1%
3551368
 
1.1%
3304351
 
1.0%
3352346
 
1.0%
3926335
 
1.0%
3350321
 
0.9%
Other values (501)29367
86.6%
ValueCountFrequency (%)
30027
 
< 0.1%
301182
0.2%
3012177
0.5%
301346
 
0.1%
301513
 
< 0.1%
ValueCountFrequency (%)
399620
 
0.1%
399520
 
0.1%
39925
 
< 0.1%
398810
 
< 0.1%
398450
0.1%

ProgramType
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
HBRA
18427 
LBRA
15490 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters135668
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHBRA
2nd rowHBRA
3rd rowHBRA
4th rowHBRA
5th rowHBRA
ValueCountFrequency (%)
HBRA18427
54.3%
LBRA15490
45.7%
2021-03-23T14:55:36.853537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:36.934558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hbra18427
54.3%
lbra15490
45.7%

Most occurring characters

ValueCountFrequency (%)
B33917
25.0%
R33917
25.0%
A33917
25.0%
H18427
13.6%
L15490
11.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter135668
100.0%

Most frequent character per category

ValueCountFrequency (%)
B33917
25.0%
R33917
25.0%
A33917
25.0%
H18427
13.6%
L15490
11.4%

Most occurring scripts

ValueCountFrequency (%)
Latin135668
100.0%

Most frequent character per script

ValueCountFrequency (%)
B33917
25.0%
R33917
25.0%
A33917
25.0%
H18427
13.6%
L15490
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII135668
100.0%

Most frequent character per block

ValueCountFrequency (%)
B33917
25.0%
R33917
25.0%
A33917
25.0%
H18427
13.6%
L15490
11.4%

ResponsibleCouncil
Categorical

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)0.8%
Missing31949
Missing (%)94.2%
Memory size265.1 KiB
boroondara
346 
maribyrnong
182 
yarra
169 
whittlesea
168 
moonee_valley
154 
Other values (10)
949 

Length

Max length15
Median length10
Mean length9.222560976
Min length4

Characters and Unicode

Total characters18150
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowboroondara
2nd rowboroondara
3rd rowboroondara
4th rowboroondara
5th rowboroondara
ValueCountFrequency (%)
boroondara346
 
1.0%
maribyrnong182
 
0.5%
yarra169
 
0.5%
whittlesea168
 
0.5%
moonee_valley154
 
0.5%
mornington144
 
0.4%
casey137
 
0.4%
kingston135
 
0.4%
manningham119
 
0.4%
yarra_ranges111
 
0.3%
Other values (5)303
 
0.9%
(Missing)31949
94.2%
2021-03-23T14:55:37.188516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boroondara346
17.6%
maribyrnong182
9.2%
yarra169
8.6%
whittlesea168
8.5%
moonee_valley154
7.8%
mornington144
7.3%
casey137
 
7.0%
kingston135
 
6.9%
manningham119
 
6.0%
yarra_ranges111
 
5.6%
Other values (5)303
15.4%

Most occurring characters

ValueCountFrequency (%)
a2558
14.1%
n2207
12.2%
r2048
11.3%
o2016
11.1%
e1492
 
8.2%
g883
 
4.9%
y860
 
4.7%
m801
 
4.4%
i748
 
4.1%
l743
 
4.1%
Other values (12)3794
20.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17821
98.2%
Connector Punctuation329
 
1.8%

Most frequent character per category

ValueCountFrequency (%)
a2558
14.4%
n2207
12.4%
r2048
11.5%
o2016
11.3%
e1492
8.4%
g883
 
5.0%
y860
 
4.8%
m801
 
4.5%
i748
 
4.2%
l743
 
4.2%
Other values (11)3465
19.4%
ValueCountFrequency (%)
_329
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17821
98.2%
Common329
 
1.8%

Most frequent character per script

ValueCountFrequency (%)
a2558
14.4%
n2207
12.4%
r2048
11.5%
o2016
11.3%
e1492
8.4%
g883
 
5.0%
y860
 
4.8%
m801
 
4.5%
i748
 
4.2%
l743
 
4.2%
Other values (11)3465
19.4%
ValueCountFrequency (%)
_329
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18150
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2558
14.1%
n2207
12.2%
r2048
11.3%
o2016
11.1%
e1492
 
8.2%
g883
 
4.9%
y860
 
4.7%
m801
 
4.4%
i748
 
4.1%
l743
 
4.1%
Other values (12)3794
20.9%

SingleOrMultipleTrees
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing31728
Missing (%)93.5%
Memory size265.1 KiB
multiple
1149 
single
1040 

Length

Max length8
Median length8
Mean length7.049794427
Min length6

Characters and Unicode

Total characters15432
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle
2nd rowsingle
3rd rowsingle
4th rowsingle
5th rowsingle
ValueCountFrequency (%)
multiple1149
 
3.4%
single1040
 
3.1%
(Missing)31728
93.5%
2021-03-23T14:55:37.449876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T14:55:37.542962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
multiple1149
52.5%
single1040
47.5%

Most occurring characters

ValueCountFrequency (%)
l3338
21.6%
i2189
14.2%
e2189
14.2%
m1149
 
7.4%
u1149
 
7.4%
t1149
 
7.4%
p1149
 
7.4%
s1040
 
6.7%
n1040
 
6.7%
g1040
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15432
100.0%

Most frequent character per category

ValueCountFrequency (%)
l3338
21.6%
i2189
14.2%
e2189
14.2%
m1149
 
7.4%
u1149
 
7.4%
t1149
 
7.4%
p1149
 
7.4%
s1040
 
6.7%
n1040
 
6.7%
g1040
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Latin15432
100.0%

Most frequent character per script

ValueCountFrequency (%)
l3338
21.6%
i2189
14.2%
e2189
14.2%
m1149
 
7.4%
u1149
 
7.4%
t1149
 
7.4%
p1149
 
7.4%
s1040
 
6.7%
n1040
 
6.7%
g1040
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII15432
100.0%

Most frequent character per block

ValueCountFrequency (%)
l3338
21.6%
i2189
14.2%
e2189
14.2%
m1149
 
7.4%
u1149
 
7.4%
t1149
 
7.4%
p1149
 
7.4%
s1040
 
6.7%
n1040
 
6.7%
g1040
 
6.7%

SpanID1
Real number (ℝ≥0)

MISSING
SKEWED

Distinct27738
Distinct (%)88.7%
Missing2631
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean15134576.99
Minimum12
Maximum2601768085
Zeros0
Zeros (%)0.0%
Memory size265.1 KiB
2021-03-23T14:55:37.654287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile24346
Q11106705.25
median3313262
Q330327462.5
95-th percentile33012795.5
Maximum2601768085
Range2601768073
Interquartile range (IQR)29220757.25

Descriptive statistics

Standard deviation78739174.62
Coefficient of variation (CV)5.202601611
Kurtosis1039.383339
Mean15134576.99
Median Absolute Deviation (MAD)2576717
Skewness31.73289658
Sum4.735003756 × 1011
Variance6.19985762 × 1015
MonotocityNot monotonic
2021-03-23T14:55:37.816224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88238699
 
< 0.1%
88314858
 
< 0.1%
11106918
 
< 0.1%
110079797
 
< 0.1%
25054596
 
< 0.1%
13057336
 
< 0.1%
11106966
 
< 0.1%
111100336
 
< 0.1%
11038425
 
< 0.1%
7342985
 
< 0.1%
Other values (27728)31220
92.0%
(Missing)2631
 
7.8%
ValueCountFrequency (%)
121
< 0.1%
561
< 0.1%
581
< 0.1%
591
< 0.1%
652
< 0.1%
ValueCountFrequency (%)
26017680851
< 0.1%
26017640011
< 0.1%
26017595991
< 0.1%
26017503051
< 0.1%
26017240821
< 0.1%

SpanID2
Real number (ℝ≥0)

MISSING
SKEWED

Distinct28055
Distinct (%)89.7%
Missing2647
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean15096051.95
Minimum12
Maximum2601764001
Zeros0
Zeros (%)0.0%
Memory size265.1 KiB
2021-03-23T14:55:37.978094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile26481.45
Q11106974.25
median3316308.5
Q330328338.75
95-th percentile33014602
Maximum2601764001
Range2601763989
Interquartile range (IQR)29221364.5

Descriptive statistics

Standard deviation76140235.59
Coefficient of variation (CV)5.043718439
Kurtosis1104.681111
Mean15096051.95
Median Absolute Deviation (MAD)2712303
Skewness32.62134889
Sum4.720535445 × 1011
Variance5.797335476 × 1015
MonotocityNot monotonic
2021-03-23T14:55:38.140002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
882387710
 
< 0.1%
11106909
 
< 0.1%
11103208
 
< 0.1%
88239278
 
< 0.1%
88267487
 
< 0.1%
110080626
 
< 0.1%
11067476
 
< 0.1%
13164435
 
< 0.1%
330691324
 
< 0.1%
9151044
 
< 0.1%
Other values (28045)31203
92.0%
(Missing)2647
 
7.8%
ValueCountFrequency (%)
121
< 0.1%
511
< 0.1%
561
< 0.1%
581
< 0.1%
651
< 0.1%
ValueCountFrequency (%)
26017640011
< 0.1%
26017240872
< 0.1%
26017240821
< 0.1%
26016776371
< 0.1%
26016776361
< 0.1%

SpanLength
Real number (ℝ≥0)

MISSING
SKEWED

Distinct325
Distinct (%)8.0%
Missing29857
Missing (%)88.0%
Infinite0
Infinite (%)0.0%
Mean69.28866995
Minimum5
Maximum4461
Zeros0
Zeros (%)0.0%
Memory size265.1 KiB
2021-03-23T14:55:38.301851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q137
median46
Q363
95-th percentile221.05
Maximum4461
Range4456
Interquartile range (IQR)26

Descriptive statistics

Standard deviation99.22458379
Coefficient of variation (CV)1.432046305
Kurtosis977.2475425
Mean69.28866995
Median Absolute Deviation (MAD)11
Skewness23.88842538
Sum281312
Variance9845.518029
MonotocityNot monotonic
2021-03-23T14:55:38.443455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45158
 
0.5%
44149
 
0.4%
40133
 
0.4%
36119
 
0.4%
43118
 
0.3%
42107
 
0.3%
39106
 
0.3%
47104
 
0.3%
37103
 
0.3%
41103
 
0.3%
Other values (315)2860
 
8.4%
(Missing)29857
88.0%
ValueCountFrequency (%)
51
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
124
< 0.1%
ValueCountFrequency (%)
44611
< 0.1%
19331
< 0.1%
5951
< 0.1%
5391
< 0.1%
5331
< 0.1%

SpanVoltages
Categorical

HIGH CORRELATION
MISSING

Distinct27
Distinct (%)1.1%
Missing31461
Missing (%)92.8%
Memory size265.1 KiB
LV
1194 
HV
773 
HV,LV
156 
LV,HV
120 
SWER
 
81
Other values (22)
132 

Length

Max length15
Median length2
Mean length2.707247557
Min length2

Characters and Unicode

Total characters6649
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.4%

Sample

1st rowLV
2nd rowLV
3rd rowLV
4th rowHV
5th rowLV
ValueCountFrequency (%)
LV1194
 
3.5%
HV773
 
2.3%
HV,LV156
 
0.5%
LV,HV120
 
0.4%
SWER81
 
0.2%
insulated46
 
0.1%
6627
 
0.1%
LV,insulated12
 
< 0.1%
HV,669
 
< 0.1%
HV,insulated7
 
< 0.1%
Other values (17)31
 
0.1%
(Missing)31461
92.8%
2021-03-23T14:55:38.767395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lv1194
48.6%
hv773
31.5%
hv,lv156
 
6.4%
lv,hv120
 
4.9%
swer81
 
3.3%
insulated46
 
1.9%
6627
 
1.1%
lv,insulated12
 
0.5%
hv,669
 
0.4%
hv,insulated7
 
0.3%
Other values (17)31
 
1.3%

Most occurring characters

ValueCountFrequency (%)
V2589
38.9%
L1505
22.6%
H1084
16.3%
,342
 
5.1%
698
 
1.5%
S87
 
1.3%
e83
 
1.2%
W82
 
1.2%
E82
 
1.2%
R82
 
1.2%
Other values (17)615
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5527
83.1%
Lowercase Letter677
 
10.2%
Other Punctuation342
 
5.1%
Decimal Number98
 
1.5%
Connector Punctuation5
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e83
12.3%
i77
11.4%
t73
10.8%
n72
10.6%
s72
10.6%
u72
10.6%
l72
10.6%
a72
10.6%
d72
10.6%
v5
 
0.7%
Other values (3)7
 
1.0%
ValueCountFrequency (%)
V2589
46.8%
L1505
27.2%
H1084
19.6%
S87
 
1.6%
W82
 
1.5%
E82
 
1.5%
R82
 
1.5%
A5
 
0.1%
B5
 
0.1%
C5
 
0.1%
ValueCountFrequency (%)
,342
100.0%
ValueCountFrequency (%)
698
100.0%
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6204
93.3%
Common445
 
6.7%

Most frequent character per script

ValueCountFrequency (%)
V2589
41.7%
L1505
24.3%
H1084
17.5%
S87
 
1.4%
e83
 
1.3%
W82
 
1.3%
E82
 
1.3%
R82
 
1.3%
i77
 
1.2%
t73
 
1.2%
Other values (14)460
 
7.4%
ValueCountFrequency (%)
,342
76.9%
698
 
22.0%
_5
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6649
100.0%

Most frequent character per block

ValueCountFrequency (%)
V2589
38.9%
L1505
22.6%
H1084
16.3%
,342
 
5.1%
698
 
1.5%
S87
 
1.3%
e83
 
1.2%
W82
 
1.2%
E82
 
1.2%
R82
 
1.2%
Other values (17)615
 
9.2%

TemperatureRange
Categorical

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)0.2%
Missing25989
Missing (%)76.6%
Memory size265.1 KiB
0_15
2322 
15_20
1799 
16_20
1032 
10_15
775 
20_25
701 
Other values (9)
1299 

Length

Max length7
Median length5
Mean length4.706609485
Min length4

Characters and Unicode

Total characters37314
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowover_40
2nd row0_15
3rd row16_20
4th row16_20
5th row16_20
ValueCountFrequency (%)
0_152322
 
6.8%
15_201799
 
5.3%
16_201032
 
3.0%
10_15775
 
2.3%
20_25701
 
2.1%
21_25443
 
1.3%
25_30317
 
0.9%
30_35206
 
0.6%
26_30184
 
0.5%
31_3548
 
0.1%
Other values (4)101
 
0.3%
(Missing)25989
76.6%
2021-03-23T14:55:39.030974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0_152322
29.3%
15_201799
22.7%
16_201032
13.0%
10_15775
 
9.8%
20_25701
 
8.8%
21_25443
 
5.6%
25_30317
 
4.0%
30_35206
 
2.6%
26_30184
 
2.3%
31_3548
 
0.6%
Other values (4)101
 
1.3%

Most occurring characters

ValueCountFrequency (%)
_7928
21.2%
07437
19.9%
17222
19.4%
56681
17.9%
25620
15.1%
61235
 
3.3%
31070
 
2.9%
473
 
0.2%
o12
 
< 0.1%
v12
 
< 0.1%
Other values (2)24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29338
78.6%
Connector Punctuation7928
 
21.2%
Lowercase Letter48
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
07437
25.3%
17222
24.6%
56681
22.8%
25620
19.2%
61235
 
4.2%
31070
 
3.6%
473
 
0.2%
ValueCountFrequency (%)
o12
25.0%
v12
25.0%
e12
25.0%
r12
25.0%
ValueCountFrequency (%)
_7928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common37266
99.9%
Latin48
 
0.1%

Most frequent character per script

ValueCountFrequency (%)
_7928
21.3%
07437
20.0%
17222
19.4%
56681
17.9%
25620
15.1%
61235
 
3.3%
31070
 
2.9%
473
 
0.2%
ValueCountFrequency (%)
o12
25.0%
v12
25.0%
e12
25.0%
r12
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII37314
100.0%

Most frequent character per block

ValueCountFrequency (%)
_7928
21.2%
07437
19.9%
17222
19.4%
56681
17.9%
25620
15.1%
61235
 
3.3%
31070
 
2.9%
473
 
0.2%
o12
 
< 0.1%
v12
 
< 0.1%
Other values (2)24
 
0.1%

VegetationSpan
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.2 KiB
True
30319 
False
3598 
ValueCountFrequency (%)
True30319
89.4%
False3598
 
10.6%
2021-03-23T14:55:39.111904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

WeatherStation
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size265.1 KiB
MOORABBIN AIRPORT
9047 
ESSENDON AIRPORT
2873 
BALLARAT AERODROME
2317 
AVALON AIRPORT
2309 
BENDIGO AIRPORT
2169 
Other values (15)
15202 

Length

Max length32
Median length17
Mean length17.88501342
Min length13

Characters and Unicode

Total characters606606
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBENDIGO AIRPORT
2nd rowBENDIGO AIRPORT
3rd rowBENDIGO AIRPORT
4th rowBENDIGO AIRPORT
5th rowBENDIGO AIRPORT
ValueCountFrequency (%)
MOORABBIN AIRPORT9047
26.7%
ESSENDON AIRPORT2873
 
8.5%
BALLARAT AERODROME2317
 
6.8%
AVALON AIRPORT2309
 
6.8%
BENDIGO AIRPORT2169
 
6.4%
MELBOURNE AIRPORT1971
 
5.8%
MORWELL (LATROBE VALLEY AIRPORT)1970
 
5.8%
WANGARATTA AERO1721
 
5.1%
BAIRNSDALE AIRPORT1602
 
4.7%
SWAN HILL AERODROME1234
 
3.6%
Other values (10)6704
19.8%
2021-03-23T14:55:39.365258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
airport26224
34.7%
moorabbin9047
 
12.0%
aerodrome4245
 
5.6%
essendon2873
 
3.8%
ballarat2317
 
3.1%
avalon2309
 
3.1%
bendigo2169
 
2.9%
melbourne1971
 
2.6%
latrobe1970
 
2.6%
valley1970
 
2.6%
Other values (23)20469
27.1%

Most occurring characters

ValueCountFrequency (%)
R90371
14.9%
A79949
13.2%
O75121
12.4%
I43432
 
7.2%
41647
 
6.9%
T36585
 
6.0%
E33745
 
5.6%
N31530
 
5.2%
L30606
 
5.0%
P29750
 
4.9%
Other values (15)113870
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter557507
91.9%
Space Separator41647
 
6.9%
Open Punctuation3726
 
0.6%
Close Punctuation3726
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
R90371
16.2%
A79949
14.3%
O75121
13.5%
I43432
7.8%
T36585
6.6%
E33745
 
6.1%
N31530
 
5.7%
L30606
 
5.5%
P29750
 
5.3%
B29408
 
5.3%
Other values (12)77010
13.8%
ValueCountFrequency (%)
41647
100.0%
ValueCountFrequency (%)
(3726
100.0%
ValueCountFrequency (%)
)3726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin557507
91.9%
Common49099
 
8.1%

Most frequent character per script

ValueCountFrequency (%)
R90371
16.2%
A79949
14.3%
O75121
13.5%
I43432
7.8%
T36585
6.6%
E33745
 
6.1%
N31530
 
5.7%
L30606
 
5.5%
P29750
 
5.3%
B29408
 
5.3%
Other values (12)77010
13.8%
ValueCountFrequency (%)
41647
84.8%
(3726
 
7.6%
)3726
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII606606
100.0%

Most frequent character per block

ValueCountFrequency (%)
R90371
14.9%
A79949
13.2%
O75121
12.4%
I43432
 
7.2%
41647
 
6.9%
T36585
 
6.0%
E33745
 
5.6%
N31530
 
5.2%
L30606
 
5.0%
P29750
 
4.9%
Other values (15)113870
18.8%

Interactions

2021-03-23T14:55:18.414288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:18.583673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:18.730854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:18.880282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.019774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.155534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.298966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.440398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.582145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.721687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:19.863303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.098349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.236484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.383623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.548964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.687533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.841922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:20.983035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.119679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.260448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.404127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.545801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.681141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.832669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:21.972211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:22.111620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:22.241961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:22.365449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:22.494832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T14:55:22.616329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-23T14:55:39.496824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T14:55:39.668929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T14:55:39.840994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T14:55:40.063636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T14:55:40.569726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-23T14:55:23.068329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T14:55:25.397949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-23T14:55:26.587091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-23T14:55:27.899333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

AdditionalInformationAddressClearanceRangeClearanceSpaceCouncilAdditionalInformationCouncilClearanceRangeCouncilClearanceSpaceCouncilElectricLineContactCouncilGenusCouncilNonComplianceCodeCouncilOtherInfrastructurePresentCouncilSingleOrMultipleTreesCouncilSpanVoltagesElectricLineContactElectricalAssetsFinancialYearFireHazardDeclaredStatusGenusHazardAssessmentInspectionDatetimeLatLocalityLocationFeederLongNetworkTypeNonComplianceCodeNonCompliantOtherInfrastructurePresentPostcodeProgramTypeResponsibleCouncilSingleOrMultipleTreesSpanID1SpanID2SpanLengthSpanVoltagesTemperatureRangeVegetationSpanWeatherStation
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes22:00.0-36.589230YARRABERBEHK024144.055425PCNaNnoNaN3516HBRANaNNaN33022395.030295650.0NaNNaNNaNyesBENDIGO AIRPORT
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes23:00.0-36.589225YARRABERBEHK024144.053142PCNaNnoNaN3516HBRANaNNaN33019538.033022395.0NaNNaNNaNyesBENDIGO AIRPORT
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV,transformer2018-19HBRA_nonNaNyes24:00.0-36.589119YARRABERBEHK024144.051735PCNaNnoNaN3516HBRANaNNaN32039321.033019538.0NaNNaNNaNyesBENDIGO AIRPORT
3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLV2018-19HBRA_nonNaNyes26:00.0-36.589166YARRABERBEHK024144.050919PCNaNnoNaN3516HBRANaNNaN30029869.032039321.0NaNNaNNaNyesBENDIGO AIRPORT
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSWER,transformer2018-19HBRA_nonNaNyes29:00.0-36.654243WHIPSTICKEHK023144.272788PCNaNnoNaN3556HBRANaNNaN32039108.030029697.0NaNNaNNaNyesBENDIGO AIRPORT
5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSWER2018-19HBRA_nonNaNyes30:00.0-36.652924WHIPSTICKEHK023144.272785PCNaNnoNaN3556HBRANaNNaN30029697.030029689.0NaNNaNNaNyesBENDIGO AIRPORT
6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSWER,transformer2018-19HBRA_nonNaNyes33:00.0-36.651523WHIPSTICKEHK023144.275923PCNaNnoNaN3556HBRANaNNaN30029689.032039085.0NaNNaNNaNyesBENDIGO AIRPORT
7NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSWER2018-19HBRA_nonNaNyes36:00.0-36.650428WHIPSTICKEHK023144.278292PCNaNnoNaN3556HBRANaNNaN32039085.032039078.0NaNNaNNaNyesBENDIGO AIRPORT
8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSWER,transformer2018-19HBRA_nonNaNyes38:00.0-36.649350WHIPSTICKEHK023144.281292PCNaNnoNaN3556HBRANaNNaN32039078.030331995.0NaNNaNNaNyesBENDIGO AIRPORT
9NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes43:00.0-36.343099BEARS LAGOONEHK024143.950946PCNaNnoNaN3517HBRANaNNaN30029347.030028857.0NaNNaNNaNyesBENDIGO AIRPORT

Last rows

AdditionalInformationAddressClearanceRangeClearanceSpaceCouncilAdditionalInformationCouncilClearanceRangeCouncilClearanceSpaceCouncilElectricLineContactCouncilGenusCouncilNonComplianceCodeCouncilOtherInfrastructurePresentCouncilSingleOrMultipleTreesCouncilSpanVoltagesElectricLineContactElectricalAssetsFinancialYearFireHazardDeclaredStatusGenusHazardAssessmentInspectionDatetimeLatLocalityLocationFeederLongNetworkTypeNonComplianceCodeNonCompliantOtherInfrastructurePresentPostcodeProgramTypeResponsibleCouncilSingleOrMultipleTreesSpanID1SpanID2SpanLengthSpanVoltagesTemperatureRangeVegetationSpanWeatherStation
33907NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV,transformer2018-19HBRA_nonNaNyes11:00.0-36.603747YARRABERBEHK024144.061881PCNaNnoNaN3516HBRANaNNaN32040191.032040184.0NaNNaNNaNyesBENDIGO AIRPORT
33908NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes12:00.0-36.603733YARRABERBEHK024144.059842PCNaNNaNNaN3516HBRANaNNaN30030492.030030485.0NaNNaNNaNnoBENDIGO AIRPORT
33909NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes12:00.0-36.603751YARRABERBEHK024144.061141PCNaNnoNaN3516HBRANaNNaN30030485.032040191.0NaNNaNNaNyesBENDIGO AIRPORT
33910NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLV2018-19HBRA_nonNaNyes12:00.0-36.390096SERPENTINEEHK024143.953803PCNaNnoNaN3517HBRANaNNaN32037513.030028656.0NaNNaNNaNyesBENDIGO AIRPORT
33911NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes13:00.0-36.603728YARRABERBEHK024144.058392PCNaNNaNNaN3516HBRANaNNaN30030495.030030492.0NaNNaNNaNnoBENDIGO AIRPORT
33912NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLV2018-19HBRA_nonNaNyes17:00.0-36.388960SERPENTINEEHK024143.955237PCNaNnoNaN3517HBRANaNNaN30028656.032037499.0NaNNaNNaNyesBENDIGO AIRPORT
33913NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNLV2018-19HBRA_nonNaNyes18:00.0-36.387164SERPENTINEEHK024143.955752PCNaNnoNaN3517HBRANaNNaN30319983.032037499.0NaNNaNNaNyesBENDIGO AIRPORT
33914NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNinsulated2018-19HBRA_nonNaNyes19:00.0-36.386611SERPENTINEEHK024143.955409PCNaNNaNNaN3517HBRANaNNaN30319983.030342905.0NaNNaNNaNnoBENDIGO AIRPORT
33915NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes20:00.0-36.386611SERPENTINEEHK024143.957898PCNaNnoNaN3517HBRANaNNaN30342905.033021333.0NaNNaNNaNyesBENDIGO AIRPORT
33916NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNHV2018-19HBRA_nonNaNyes21:00.0-36.589117YARRABERBEHK024144.059068PCNaNNaNNaN3516HBRANaNNaN30295650.032039298.0NaNNaNNaNnoBENDIGO AIRPORT